In this paper we examine the prevalence of data, specification, and parameter uncertainty in the formation of simple rules that mimic monetary policymaking decisions. Our approach is to build real-time data sets and simulate a real-time policy-setting environment in which we assume that policy is captured by movements in the actual federal funds rate, and then to assess what sorts of policy rule models and what sorts of data best explain what the Federal Reserve actually did.
A consumer's decision to engage in search depends on the beliefs the consumer has about an unknown product characteristic such as price. In this paper, we elicit the distribution of price beliefs and explicitly study their role in a consumer's decision to search.
In this paper, we propose a research agenda for psychologists in general, and scholars of culture and negotiations in particular, to address the key challenges of dealing with an increasingly globalized world from a psychological perspective. Building on an understanding of globalization in terms of cultural and subjective matters, we propose three research domains in which psychology scholars can contribute to a further understanding of our global society: (a) the effects of global contact on cognition and behavior; (b) hybridization and human agency; and (c) new forms of cooperation.
In this paper, we empirically analyze the determinants of excess inventory announcement and the stock market reaction to the announcement in the US retail sector. We examine if the firm’s operational competence, as measured by total factor productivity (TFP), can explain the retailer’s excess inventory announcement. We also investigate if the stock market reaction to such announcements is conditional on the operational competence of the announcing firm. We use a combined dataset on excess inventory announcements, annual financial statements, and daily stock prices of publicly traded retailers in the USA between 1990 and 2011.
Many of the most prosperous places in the U.S. are hotbeds of technology and also the home bases of companies which exercise monopoly power across much larger territories – nationally, or even globally. This paper makes four arguments about regional income disparities.
To increase revenue or improve customer service, companies are increasingly personalizing their product or service offerings based on their customers' history of interactions. In this paper, we show how call centers can improve customer service by implementing personalized priority policies.
Time series regression analysis relies on the heteroskedasticity- and auto-correlation-consistent (HAC) estimation of the asymptotic variance to conduct proper inference. This paper develops such inferential methods for high-dimensional time series regressions.
Despite extensive empirical evidence of the economic and financial benefits of green buildings, energy retrofit investments in existing buildings have not reached widespread adoption.This paper empirically estimate returns to energy retrofit investments for multifamily and commercial buildings in New York City, using a novel database of actual audit report recommendations and permitted renovation work extracted using natural language processing.
Background: Influenza imposes heavy societal costs through healthcare expenditures, missed days of work, and numerous hospitalizations each year. Considering these costs, the healthcare and behavioral science literature offers suggestions on increasing demand for flu vaccinations. And yet, the adult flu vaccination rate fluctuated between 37% and 46% between 2010 and 2019.
Aim: Although a demand-side approach represents one viable strategy, an operations management approach would also highlight the need to consider a supply-side approach. In this paper, we investigate how to improve clinic vaccination rates by altering provider behavior.
The financial industry has eagerly adopted machine learning algorithms to improve on traditional predictive models. In this paper we caution against blindly applying such techniques. We compare forecasting ability of machine learning methods in evaluating future payoffs on synthetic variance swaps.
This paper evaluates the pros and cons of including private equity fund investments in defined contribution plans. Potential benefits include higher returns and improved diversification as well as a relatively safe method for accessing investments previously only available to institutions and the very wealthy. Despite these enticing benefits, they need to be weighed against potential challenges and costs that may arise from creating this broader access to private funds. The complicated structure and uncertainty around the mechanism to provide required liquidity backstops may bring increased fees or even disrupt the private fund model.
The idea that new ventures are simple mimetic reflections of the organizational practices of existing organizations contradicts the recognized importance of organizational diversity for innovation. There is an inherent contradiction in the literature between the persistence implied by the inheritance of practices from prior employment, and the experimentation prevalent in the organizational practices contributed by new organizations. This paper first accounts for mechanisms that may drive heritage of practices from parent organizations to their spawns.
Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets. While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change. In this paper we discuss the barriers that inhibit scientists from measuring the effects of AI and automation on the future of work.
Increased consumer demand for healthier product options and looming regulation have prompted many consumer goods brands to adjust the amount of sugar content in their product lines, including adding products with reduced sugar content or smaller package sizes. Even as brands adopt such practices, little guidance exists for how they should do so to protect or enhance their brand performance. This paper studies whether and when sugar reduction strategies affect sales.
We examine the effect of pay transparency on gender pay gap and firm outcomes. This paper exploits a 2006 legislation change in Denmark that requires firms to provide gender disaggregated wage statistics. Using detailed employee-employer administrative data and a difference-in-differences and difference-in-discontinuities designs, we find the law reduces the gender pay gap, primarily by slowing the wage growth for male employees.
This paper uses two large panel data sets in China to study the effects of a health shock on household income mobility from 1991 to 2016. We compare outcomes of households with a member who receives a health shock with comparable households that do not receive any health shocks.
This paper experimentally tests the Fox-Tversky (1995) source preference hypothesis as axiomatized in Chew and Sagi (2008) where people may have preference between equally distributed risks depending on the underlying sources of uncertainty.
The paper introduces structured machine learning regressions for heavy-tailed dependent panel data potentially sampled at different frequencies. We focus on the sparse-group LASSO regularization. This type of regularization can take advantage of the mixed frequency time series panel data structures and improve the quality of the estimates.
High levels of inflation have dominated global headlines for a good part of the last year, but what’s the connection between high global inflation and a strong dollar?
In this paper, we develop new methods for analyzing high-dimensional tensor datasets. A tensor factor model describes a high-dimensional dataset as a sum of a low-rank component and an idiosyncratic noise, generalizing traditional factor models for panel data. We propose an estimation algorithm, called tensor principal component analysis (PCA), which generalizes the traditional PCA applicable to panel data.